Expertise in expert systems: knowledge acquisition for biological expert systems

In this paper it is argued that an expert system requires morethan factual knowledge before it can display expertise in agiven domain. The additional knowledge consists of the heuristicsor ‘rules of thumb’ used by an expert to manipulateand interpret the factual knowledge. The knowledge acquisitionphase of an expert system project involves determining the factualknowledge (which may be obtained from published sources) andthe heuristics used by an expert to manipulate that knowledge-theseheuristics can only be obtained from an expert. In reviewingexisting biological expert systems it is apparent that manycontain only the factual knowledge relating to the domain, andlack the heuristics that enable such systems to show expertise.This paper reviews a number of knowledge acquisition techniqueswhich could be used for acquiring heuristic knowledge and discusseswhen their use is appropriate. The knowledge acquisition techniquesdiscussed are those suitable for the development of small-scaleexpert systems as these are most likely to be of interest tobiologists. The techniques include the use of questionnaires,interview techniques and protocol analysis; particular emphasisis placed on a mod cation to the ‘twenty questions’interview technique which was developed specifically to elicittaxonomic knowledge relating to water mite identification.     

MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

MetaReg http://acgt.cs.tau.ac.il/metareg/application.html is a computational tool that models cellular networks and integrates experimental results with such models. MetaReg represents established knowledge about a biological system, available today mostly in informal form in the literature, as probabilistic network models with underlying combinatorial regulatory logic. MetaReg enables contrasting predictions with measurements, model improvements and studying what-if scenarios. By summarizing prior knowledge and providing visual and computational aids, it helps the expert explore and understand her system better.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

A structural equation modeling approach for formalizing and evaluating ecological integrity in terrestrial ecosystems

Ecological integrity is a functional property that integrates habitat functions and species information for maintaining key ecological interactions in predator-prey systems. As a functional property, ecological integrity can be modeled as a latent concept from observable spatial attributes that measure the ecosystem's capacity to provide suitable habitat conditions for apex predators. Ecological integrity is a tri-dimensional concept that stems from “stable”, “concurrent” and “intact” conditions. A theoretical framework and a methodology is presented here for modeling ecological integrity from observable attributes (as GIS layers) to obtain a spatial representation of the integrity condition. From a theoretical framework, the ecological integrity concept is obtained with a structural equation modeling approach, where several other latent variables are obtained for characterizing a hierarchical network of spatial information. Later on, these observable attributes, and several latent modeled variables are translated into sources of geographic information that can be used to monitor changes in the natural remnant areas due to human impacts. When examining the direct, indirect and total effects of habitat loss and fragmentation on ecological integrity, spatial intactness (e.g., the amount of remnant habitat and connectivity) and stability (resistance in the interaction network and mobile links) are the attributes more affected by the pathway effects. The balance of the formative parameters obtained for the model supports the idea that ecosystems that have a high degree of integrity should maintain a high level of stability, self-organization and naturalness. These attributes are achieved when spatial habitat intactness and species interactions are maintained.     

SYCAMORE--a systems biology computational analysis and modeling research environment

SYCAMORE is a browser-based application that facilitates construction, simulation and analysis of kinetic models in systems biology. Thus, it allows e.g. database supported modelling, basic model checking and the estimation of unknown kinetic parameters based on protein structures. In addition, it offers some guidance in order to allow non-expert users to perform basic computational modelling tasks. AVAILABILITY: SYCAMORE is freely available for academic use at http://sycamore.eml.org. Commercial users may acquire a license. CONTACT: ursula.kummer@bioquant.uni-heidelberg.de.     

Ontologies: Formalising biological knowledge for bioinformatics

An ontology is a domain of knowledge structured through formal rules so that it can be interpreted and used by computers. Ontologies are becoming increasingly important in bioinformatics because they can be linked to the information in databases and their knowledge then used to query the databases. Typical examples in current use are the Gene Ontology, which incorporates much of our knowledge about gene products, and ontologies of developmental anatomy, which, for example, facilitate tissue-based queries to gene expression databases both textually and spatially. This article considers the production, formulation and types of bio-ontologies together with the reasons why they are so useful.     

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.     

Copper trafficking in eukaryotic systems: current knowledge from experimental and computational efforts

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Review of stochastic hybrid systems with applications in biological systems modeling and analysis

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.     

BioLingua: a programmable knowledge environment for biologists

SUMMARY: BioLingua is an interactive, web-based programming environment that enables biologists to analyze biological systems by combining knowledge and data through direct end-user programming. BioLingua embeds a mature symbolic programming language in a frame-based knowledge environment, integrating genomic and pathway knowledge about a class of similar organisms. The BioLingua language provides interfaces to numerous state-of-the-art bioinformatic tools, making these available as an integrated package through the novel use of web-based programmability and an integrated Wiki-based community code and data store. The pilot instantiation of BioLingua, which has been developed in collaboration with several cyanobacteriologists, integrates knowledge about a subset of cyanobacteria with the Gene Ontology, KEGG and BioCyc knowledge bases. We introduce the BioLingua concept, architecture and language, and give several examples of its use in complex analyses. AVAILABILITY: Extensive documentation is available online at http://nostoc.stanford.edu/Docs/index.html CONTACT: JShrager@Stanford.edu     

Ethanol utilization by sulfate-reducing bacteria: an experimental and modeling study

A mixed culture of sulfate-reducing bacteria containing the species Desulfovibrio desulfuricans was used to study sulfate-reduction stoichiometry and kinetics using ethanol as the carbon source. Growth yield was lower, and kinetics were slower, for ethanol compared to lactate. Ethanol was converted into acetate and no significant carbon dioxide production was observed. A mathematical model for growth of sulfate-reducing bacteria on ethanol was developed, and simulations of the growth experiments on ethanol were carried out using the model. The pH variation due to sulfate reduction, and hydrogen sulfide production and removal by nitrogen sparging, were examined. The modeling study is distinct from earlier models for systems using sulfate-reducing bacteria in that it considers growth on ethanol, and analyzes pH variations due to the product-formation reactions.     

Mechanobiological modeling of endochondral ossification: an experimental and computational analysis

Long bone formation starts early during embryonic development through a process known as endochondral ossification. This is a highly regulated mechanism that involves several mechanical and biochemical factors. Because long bone development is an extremely complex process, it is unclear how biochemical regulation is affected when dynamic loads are applied, and also how the combination of mechanical and biochemical factors affect the shape acquired by the bone during early development. In this study, we develop a mechanobiological model combining: (1) a reaction–diffusion system to describe the biochemical process and (2) a poroelastic model to determine the stresses and fluid flow due to loading. We simulate endochondral ossification and the change in long bone shapes during embryonic stages. The mathematical model is based on a multiscale framework, which consisted in computing the evolution of the negative feedback loop between Ihh/PTHrP and the diffusion of VEGF molecule (on the order of days) and dynamic loading (on the order of seconds). We compare our morphological predictions with the femurs of embryonic mice. The results obtained from the model demonstrate that pattern formation of Ihh, PTHrP and VEGF predict the development of the main structures within long bones such as the primary ossification center, the bone collar, the growth fronts and the cartilaginous epiphysis. Additionally, our results suggest high load pressures and frequencies alter biochemical diffusion and cartilage formation. Our model incorporates the biochemical and mechanical stimuli and their interaction that influence endochondral ossification during embryonic growth. The mechanobiochemical framework allows us to probe the effects of molecular events and mechanical loading on development of bone.     

Dynamic modeling of complex biological systems: a link between metabolic and macroscopic description

In this study, a class of dynamic models based on metabolic reaction pathways is analyzed, showing that systems with complex intracellular reaction networks can be represented by macroscopic reactions relating extracellular components only. Based on rigorous assumptions, the model reduction procedure is systematic and allows an equivalent 'input-output' representation of the system to be derived. The procedure is illustrated with a few examples.     

Hermitian splines for modeling biological soft tissue systems that exhibit nonlinear force-elongation curves

Numerical simulation of soft tissue mechanical properties is a critical step in developing valuable biomechanical models of live organisms. A cubic Hermitian spline optimization routine is proposed in this paper to model nonlinear experimental force-elongation curves of soft tissues, in particular when modeled as lumped elements. Boundary conditions are introduced to account for the positive definiteness and the particular curvature of the experimental curve to be fitted. The constrained least-square routine minimizes user intervention and optimizes fitting of the experimental data across the whole fitting range. The routine provides coefficients of a Hermitian spline or corresponding knots that are compatible with a number of constraints that are suitable for modeling soft tissue tensile curves. These coefficients or knots may become inputs to user-defined component properties of various modeling software. Splines are particularly advantageous over the well-known exponential model to account for the traction curve flatness at low elongations and to allow for more flexibility in the fitting process. This is desirable as soft tissue models begin to include more complex physical phenomena.     

MUCIDS: an operative C environment for acquisition and processing of polarized-light scattered from biological specimens

In this work, we describe a software package, MUCIDS, completelydeveloped in our laboratory, for acquisition and processingof differential polarizxition light-scattering data from specimensof biophysical interest. MUCIDS is a C environment that managesthe whole activity of an instrument used for measurements ofMueller matrix scattering elements. It allows one to capture,analyse, process and display data from this or from other similarlight-scattering experiments. The entire system is suitablefor routine measurements in a general biophysical (or microbiological)laboratory because of its easy handling and maintenance. Thesoftware was written in C lattice and will run on IBM personalcomputers and similar. It uses IBM/DAC and GPIB/IBM interfacecards. Received on October 3, 1989; accepted on March 26, 1990     

Toward quantitative simulation of germinal center dynamics: biological and modeling insights from experimental validation.

As models of immune system dynamics are developed, it is important to validate them with specific experimental data in order to understand their shortcomings and guide them toward becoming predictive. In this paper, we examine whether a particular mathematical model of germinal center dynamics, proposed by Oprea and Perelson, can reproduce experimental data from two specific primary responses, namely those directed against the haptens 2-phenyl-5-oxazolone and (4-hydroxy-3-nitrophenyl)acetyl. We develop formulas for estimating response-specific model parameters, as well as constraints for validating the model. In addition, we outline a general methodology for translating a continuous/deterministic model, expressed as a set of ordinary differential equations, into a discrete/stochastic framework. This methodology is used to create a new implementation of the Oprea and Perelson model that enables comparison with data on individual germinal centers. We conclude that while the model can reproduce the average dynamics of splenic germinal centers, it is at best incomplete and does not reproduce the distribution of individual germinal center behaviors. In addition to suggesting possible extensions to the model which can reconcile the dynamics with some aspects of the experimental data, we make a number of specific predictions that can be tested by in vivo experiments to obtain further insights and validation.     

On the limitations of standard statistical modeling in biological systems: A full Bayesian approach for biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.     

Diagrammatic representations for modelling biological knowledge

The contemporary research and development context in multidisciplinary biology has a serious requirement for integrating knowledge from disparate sources, and facilitating much-needed inter- and intra-disciplinary dialogue. A multiplicity of models arises when pluralistic approaches to modelling are followed, and also when there is not only a requirement to model systems and data, but also knowledge of systems and data. The challenges of addressing this multiplicity do not only include articulating the structure of complex systems, but also placing modelling within the framework of a process as well as a product. The graph representations presented here facilitate dialogue, modelling, clarification and specification of concepts, and the sharing of terms. This paper explores relationships between collections of graph representations. It is hoped that in future, when readers look at a node or a process in a graph, they will have a much deeper appreciation of relationships and context.